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Computer science pedagogy has overwhelmingly favored problem-solving practice over methods of engagement like worked-out example study especially in advanced classes. This is due to the belief that while these alternative methods may improve student conceptual learning, they may leave them less able to perform on authentic problem-solving tasks from a lack of hands-on practice. In this paper, we perform a direct comparison of this trade-off in a synchronous collaborative programming project by adjusting the boundary between problem-solving and collaborative reflection based on a worked-out example while keeping the total time on task constant. We find that the more time students spent on worked example study, the more was the observed improvement in the pre- to post-test scores with no significant difference in performance on a subsequent problem-solving task. These results, therefore, challenge the dominant place of problem-solving practice in the advanced curricular context and inform the design of collaborative programming projects at scale.

PurposeIn response to the evolving COVID-19 pandemic, many universities have transitioned to online instruction. With learning promising to be online, at least in part, for the near future, instructors may be thinking of providing online collaborative learning opportunities to their students who are increasingly isolated from their peers because of social distancing guidelines. This paper aims to provide design recommendations for online collaborative project-based learning exercises based on this research in a software engineering course at the university level.Design/methodology/approachThrough joint work between learning scientists, course instructors and software engineering practitioners, instructional design best practices of alignment between the context of the learners, the learning objectives, the task and the assessment are actualized in the design of collaborative programming projects for supporting learning. The design, first segments a short real-time collaborative exercise into tasks, each with a problem-solving phase where students participate in collaborative programming, and a reflection phase for reflecting on what they learned in the task. Within these phases, a role-assignment paradigm scaffolds collaboration by assigning groups of four students to four complementary roles that rotate after each task.FindingsBy aligning each task with granular learning objectives, significant pre- to post-test learning from the exercise as well as each task is observed.Originality/valueThe roles used in the paradigm discourage divide-and-conquer tendencies often associated with collaborative projects. By requiring students to discuss conflicting ideas to arrive at a consensus implementation, their ideas are made explicit, thus providing opportunities for clarifying misconceptions through discussion and learning from the collaboration.

Dynamic conversational agent-based support for collaborative learning has shown significant positive effects on learning over no-support or static-support control conditions in prior studies. In order to understand the boundary between human-led and AI-led support for collaboration, we compare in this study an approach where the agent’s primary role is to help students regulate their own collaboration with two more typical prompting strategies that are used only during a reflection phase: one designed to provide a specific informational focus for the reflection, and the other designed to draw out evaluation, elaboration, and exploration of alternative perspectives. Significant positive effects on learning over and above just the human-led form of support are observed when either of the prompting strategies are used.

This lightning talk presents an ongoing effort investigating the use of a collaborative programming paradigm originating in industry called Mob Programming, for effective collaborative learning in the classroom. In industry, Mob Programming involves the participation of a group of developers solving one problem at the same time and place. Developers take turns cycling through a structured process for collaboration having been assigned pre-defined roles responsible for brainstorming ideas, deciding on a path forward and implementing the consensus decision respectively. Pedagogically, there are several compelling reasons to motivate the adoption of Mob Programming in learning settings . First, the collaboration is well-structured meaning that the interaction between even a large group of students will not descend into chaos. Second, students are assigned to roles, which allows for the differentiation of responsibilities and development of skills in different aspects of solving the problem. Third, the rotation of assigned roles allows students to learn and exhibit multiple competencies as well as appreciate bringing different perspectives to bear on solving the problem. In order to investigate whether this promise is borne out in practice, the paradigm is currently being investigated in the context of a Cloud Computing course offered online to undergraduate and graduate students at a large American university and its satellite campuses. Since this effort is still underway, faculty who implement or are interested in implementing collaborative learning for this classrooms are invited to attend and provide feedback or consider joining the effort to investigate this paradigm for use in learning settings.

This paper reports on work adapting an industry standard team practice referred to as Mob Programming into a paradigm called Online Mob Programming (OMP) for the purpose of encouraging teams to reflect on concepts and share work in the midst of their project experience. We present a study situated within a series of three course projects in a large online course on Cloud Computing. In a \(3\times 3\) Latin Square design, we compare students working alone and in two OMP configurations (with and without transactivity-maximization team formation designed to enhance reflection). The analysis reveals the extent to which grading on the produced software rewards teams where highly skilled individuals dominate the work. Further, compliance with the OMP paradigm is associated with greater evidence of group reflection on concepts and greater shared practice of programming.

Prior research in Team-Based Massive Open Online Project courses (TB-MOOPs) has demonstrated both the importance of effective group composition and the potential for using automated methods for forming effective teams. Past work on automated team assignment has produced both spectacular failures and spectacular successes. In either case, different contexts pose particular challenges that may interfere with the applicability of approaches that have succeeded in other contexts. This paper reports on a case study investigating the applicability of an automated team assignment approach that has succeeded spectacularly in TB-MOOP contexts to a large online project-based course. The analysis offers both evidence of partial success of the paradigm as well as insights into areas for growth.

Wireless ad hoc networks rely on the cooperation of participating nodes to undertake activities such as routing. Malicious nodes participating in the network may refuse to forward packets and instead discard them to mount a denial-of-service attack called a packet drop or blackhole attack. Blackhole attacks can however be easily detected using common networking tools like trace route as all packets passing through the malicious node is dropped. A gray hole attack on the other hand accomplishes denial of service by selectively dropping packets thus escaping detection. In this paper, a novel two player incomplete information extensive form game is used to model the defender and the attacker both of whom are considered rational agents in an effort to determine their optimal (equilibrium) strategies under different values for the parameters true detection rate, false alarm rate, packet value, packets forwarded per unit time, probability of the node being a gray hole, cost of exposure of the attacker and cost of not using a node for the defender. The respective equilibrium strategies if followed guarantee maximum possible protection for the defender and maximal possible damage potential for the attacker.

Sentiment orientation data sets referred to variously as affective word lists, opinion lexicons, sentiment lexicons, emotion lexicons or sentiment dictionaries contain a list of words scored for the degree of positive and negative emotion they exhibit. Although these lists have been used extensively for the sentiment analysis of text data, they contain a limited number of words that are often inadequate for data obtained from modern text sources dominated by the influence of social media that has resulted in the creation and coining of new words on a regular basis. In an effort to enrich these data sets with new words, we propose two methods. The first method involves the sentiment analysis of portmanteau words. We have hypothesized that the sentiment score of a portmanteau word; which is a combination of two (or more) words and their meanings into a single new word; can be determined as a function of the sentiment scores of its component words. Regression analysis has been used to determine this functional relationship and several cases arising from the above have been evaluated on a data set constructed from SentiWordNet. The second method is an in situ approach for sentiment discovery for unknown words that uses labeled tweets and words from the sentiment orientation data set as inputs to discover the sentiment score of the unknown word. In order to validate the resultant score, we have also used a novel validation-feedback mechanism akin to cross-validation. Both these methods produce acceptable levels of accuracy proving that they can be implemented in practice.

In a variety of problem domains, it has been observed that the aggregate opinions of groups are often more accurate than those of the constituent individuals, a phenomenon that has been termed the "wisdom of the crowd." Yet, perhaps surprisingly, there is still little consensus on how generally the phenomenon holds, how best to aggregate crowd judgements, and how social influence affects estimates. We investigate these questions by taking a meta wisdom of crowds approach. With a distributed team of over 100 student researchers across 17 institutions in the United States and India, we develop a large-scale online experiment to systematically study the wisdom of crowds effect for 1,000 different tasks in 50 subject domains. These tasks involve various types of knowledge (e.g., explicit knowledge, tacit knowledge, and prediction), question formats (e.g., multiple choice and point estimation), and inputs (e.g., text, audio, and video). To examine the effect of social influence, participants are randomly assigned to one of three different experiment conditions in which they see varying degrees of information on the responses of others. In this ongoing project, we are now preparing to recruit participants via Amazon?s Mechanical Turk.

Karnatic Music (KM) is distinct because of the prevalence of gamaka — embellishments to musical notes in the form of frequency traversals. Another important aspect of KM is that the performance style is mostly extempore. Hence, Music Information Retrieval (MIR) tasks in the context of KM are highly challenging. This paper deals with the task of Audio Segmentation and its application to MIR challenges of KM at various levels. This work presents a method that incorporates a priori knowledge about the music system and the audio track at hand for segmenting the audio into its constituent notes. The method uses amplitude and energy based features to train a neural network and an accuracy of 95.2% has been achieved on KM audio samples. The paper also elucidates the application of the method to important MIR tasks such as Music Transcription and Score-Alignment in the context of KM.

Child abuse and its myriad forms often go undetected due to the geographically distributed and widespread nature of the crime. The process of reporting is also long and is often used as an excuse to allow the proliferation of these activities. To prevent the crime from going unpunished, this paper introduces a browser based web application and a mobile application called Project Jagriti that uses the power of Crowdsourcing to ensure justice for the child victims. All reports of child abuse filed using the platform are forwarded to the Child Welfare Committee (CWC); a body constituted by the Government of India to oversee child welfare and expedite the process of recovery and rehabilitation of the victims. The platform also provides anonymity for encouraging users.

In today's elementary education landscape for the blind, there exists little support for effectively visualizing concepts like figures, graphs, maps, terrains, shapes and the like. Moreover, pedagogy for the blind relies excessively on trained teachers. Current interfaces involve conversion of text to Braille (Tactile Feedback). Only text being translated however, is insufficient for a comprehensive learning experience. This paper describes a solution for this issue by introducing a redesigned low-cost tactile interface called the SmartSlate that will complement classroom education and serve as a partial substitute. The SmartSlate will support terrain and geographic projections, reiterative learning processes and educational games to enforce effective learning. As a proof of concept, an application that teaches coordinate geometry has been developed to work with the SmartSlate. This idea was well received at the Roman & Catherine Lobo School for the Blind, Kadri, Mangalore, India where our experiments will be conducted.